Ice tectonic deformation during the rapid in situ drainage of a supraglacial lake on the Greenland Ice Sheet.

We present detailed records of lake discharge, ice motion and passive seismicity capturing the behaviour and processes preceding, during and following the rapid drainage of a 4 km<sup>2</sup> supraglacial lake through 1.1-km-thick ice on the western margin of the Greenland Ice Sheet. Peak discharge of 3300 m<sup>3</sup> s<sup>−1</sup> coincident with maximal rates of vertical uplift indicates that surface water accessed the ice–bed interface causing widespread hydraulic separation and enhanced basal motion. The differential motion of four global positioning system (GPS) receivers located around the lake record the opening and closure of the fractures through which the lake drained. We hypothesise that the majority of discharge occurred through a 3-km-long fracture with a peak width averaged across its wetted length of 0.4 m. We argue that the fracture's kilometre-scale length allowed rapid discharge to be achieved by combining reasonable water velocities with sub-metre fracture widths. These observations add to the currently limited knowledge of in situ supraglacial lake drainage events, which rapidly deliver large volumes of water to the ice–bed interface

The Earth Observation Data for Habitat Monitoring (EODHaM) system

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India

Mapping Coastal Habitats in Wales

Many areas across Europe are mapped and monitored using a large range of different data types, sources and classification schemes leading to gaps in the knowledge required to fulfill the European Council?s Habitats Directive (1992). The Earth Observation Data for Habitat Monitoring (EODHaM) system, developed during the EU FP7 BioSOS project, introduces a systematic, hierarchical approach that is applicable to all sites and available as a standard, providing classifications of high value for conservation and biodiversity purposes (Lucas et al. Int J Appl Earth Observ Geoinf 37:17?28, 2015). The system is built on the Land Cover Classification System (LCCS) developed by the FAO for use in the field. The aim of this project is to generate accurate maps of the location, extent and condition of coastal Annex I habitats at Kenfig Burrows Special Area of Conservation (SAC), using VHR Worldview-2 data. Indices, such as Normalized Difference Vegetation Index (NDVI) allow straightforward visual threshold determination in the rule base, classifying LCCS Level 3 with accuracies of 90% and above. Beyond Level 3, in situ data is key for training and validating EO data to determine if (a) lifeforms/habitats are separable with the available EO data, and (b) suitable thresholds can be determined for classification. Numerous indices can be calculated, and using the GPS point training data, a separability analysis based on Analysis of Variance (ANOVA) allows those with the highest separation scores to be chosen as layers for classification. By plotting the training data sets into boxplots, suitable thresholds are determined. The appropriateness of LCCS here varies with specific sites; for example, slack habitat in sand dune ecosystems can be accurately mapped from contextual information derived from slope (calculated using VHR LiDAR data) and can therefore be translated to habitat from LCCS Level 3. Classifications are therefore translated from land cover to habitat after LCCS Level 3 instead of following the hierarchy to Level 4 and beyond. Once the broad habitat baseline is mapped, thresholds become restricting as they set clear straight lines in the feature space when classifying, therefore machine learning techniques such as random forest and/or support vector machines are more suitable for determining whether dominant species within broad habitat classes can be separated and classified accurately. By classifying dominant species, condition of habitats can be inferred. With accuracies of classifying some habitats higher than others when implementing EO data into a monitoring system, field surveying can never be ruled out to attain the knowledge required for the habitats directive. However, surveying can be applied specifically to those habitats that EO data cannot sufficiently classif

Report on RS-IUS second–stage modules software description

D5.5 describes the EODHaM 2nd stage and the translation of the EODHaM 1st, 2nd and 3rd stages to an open source environment and the sequence of processing, including feature extraction and segmentation, classification of land covers and translation to GHCs. Texture and Dempster-Shafer uncertainty analysis are also outlined. Land cover maps are presented for BIO_SOS test sites with estimates of accuracy. Report on RS-IUS second?stage modules software description (PDF Download Available). Available from: https://www.researchgate.net/publication/274704969_Report_on_RS-IUS_second-stage_modules_software_description?channel=doi&linkId=552657640cf295bf160ed603&showFulltext=true [accessed Jan 16 2018]

Land Cover and Habitat Classification from Earth Observation Data: A New Approach from BIO_SOS. GI_Forum 2013 – Creating the GISociety|

As part of the Biodiversity Multi-Source Monitoring System (BIO_SOS), a new approach to the classification of Food and Agricultural Organisation (FAO) Land Cover Classification System (LCCS) classes from very high resolution (VHR) remote sensing data has been developed. These classes are also translated to General Habitat Categories (GHCs). Examples of the classification are presented for Cors Fochno in Wales but can be generated for any site where appropriate remote sensing data have been acquired. The system has been developed for operational monitoring of protected areas and their surrounds

Land Cover and Habitat Classification from Earth Observation Data: A New Approach from BIO_SOS. GI_Forum 2013 – Creating the GISociety|

As part of the Biodiversity Multi-Source Monitoring System (BIO_SOS), a new approach to the classification of Food and Agricultural Organisation (FAO) Land Cover Classification System (LCCS) classes from very high resolution (VHR) remote sensing data has been developed. These classes are also translated to General Habitat Categories (GHCs). Examples of the classification are presented for Cors Fochno in Wales but can be generated for any site where appropriate remote sensing data have been acquired. The system has been developed for operational monitoring of protected areas and their surrounds